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ALBADDAWI
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ft-google-gemma-2b-it-qlora-v2

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Safetensors
trl
sft
Generated from Trainer
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  1. README.md +160 -196
  2. adapter_config.json +36 -0
  3. adapter_model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md CHANGED
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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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-
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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-
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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-
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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-
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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-
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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-
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- ## Training Details
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-
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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  ---
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+ license: other
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+ library_name: peft
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+ tags:
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+ - trl
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+ - sft
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+ - generated_from_trainer
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+ base_model: google/gemma-2b-it
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+ model-index:
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+ - name: ft-google-gemma-2b-it-qlora-v2
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+ results: []
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  ---
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # ft-google-gemma-2b-it-qlora-v2
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+
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+ This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 5.8028
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 10
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+ - eval_batch_size: 10
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 80
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: constant
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+ - num_epochs: 1000
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:----:|:---------------:|
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+ | 0.2955 | 10.0 | 10 | 2.7587 |
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+ | 0.232 | 20.0 | 20 | 2.5366 |
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+ | 0.177 | 30.0 | 30 | 2.4293 |
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+ | 0.1317 | 40.0 | 40 | 2.4247 |
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+ | 0.0893 | 50.0 | 50 | 2.5725 |
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+ | 0.0472 | 60.0 | 60 | 2.8254 |
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+ | 0.0147 | 70.0 | 70 | 3.2230 |
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+ | 0.0035 | 80.0 | 80 | 3.7653 |
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+ | 0.0015 | 90.0 | 90 | 4.0707 |
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+ | 0.0008 | 100.0 | 100 | 4.2730 |
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+ | 0.0006 | 110.0 | 110 | 4.3961 |
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+ | 0.0006 | 120.0 | 120 | 4.4900 |
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+ | 0.0005 | 130.0 | 130 | 4.5394 |
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+ | 0.0005 | 140.0 | 140 | 4.5999 |
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+ | 0.0005 | 150.0 | 150 | 4.6447 |
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+ | 0.0004 | 160.0 | 160 | 4.6848 |
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+ | 0.0004 | 170.0 | 170 | 4.7255 |
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+ | 0.0004 | 180.0 | 180 | 4.7569 |
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+ | 0.0004 | 190.0 | 190 | 4.7802 |
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+ | 0.0004 | 200.0 | 200 | 4.8020 |
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+ | 0.0004 | 210.0 | 210 | 4.8522 |
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+ | 0.0004 | 220.0 | 220 | 4.8690 |
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+ | 0.0004 | 230.0 | 230 | 4.8940 |
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+ | 0.0004 | 240.0 | 240 | 4.9423 |
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+ | 0.0004 | 250.0 | 250 | 4.9723 |
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+ | 0.0004 | 260.0 | 260 | 4.9644 |
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+ | 0.0004 | 270.0 | 270 | 4.9923 |
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+ | 0.0004 | 280.0 | 280 | 5.0230 |
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+ | 0.0004 | 290.0 | 290 | 5.0319 |
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+ | 0.0004 | 300.0 | 300 | 5.0627 |
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+ | 0.0004 | 310.0 | 310 | 5.1078 |
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+ | 0.0004 | 320.0 | 320 | 5.1167 |
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+ | 0.0004 | 330.0 | 330 | 5.1260 |
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+ | 0.0004 | 340.0 | 340 | 5.1586 |
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+ | 0.0004 | 350.0 | 350 | 5.1803 |
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+ | 0.0004 | 360.0 | 360 | 5.1652 |
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+ | 0.0004 | 370.0 | 370 | 5.1692 |
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+ | 0.0004 | 380.0 | 380 | 5.1980 |
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+ | 0.0004 | 390.0 | 390 | 5.2254 |
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+ | 0.0004 | 400.0 | 400 | 5.2434 |
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+ | 0.0004 | 410.0 | 410 | 5.2792 |
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+ | 0.0004 | 420.0 | 420 | 5.2699 |
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+ | 0.0004 | 430.0 | 430 | 5.2906 |
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+ | 0.0004 | 440.0 | 440 | 5.3069 |
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+ | 0.0004 | 450.0 | 450 | 5.3063 |
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+ | 0.0004 | 460.0 | 460 | 5.3275 |
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+ | 0.0004 | 470.0 | 470 | 5.3406 |
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+ | 0.0004 | 480.0 | 480 | 5.3319 |
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+ | 0.0004 | 490.0 | 490 | 5.3354 |
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+ | 0.0004 | 500.0 | 500 | 5.3601 |
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+ | 0.0004 | 510.0 | 510 | 5.4094 |
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+ | 0.0004 | 520.0 | 520 | 5.4175 |
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+ | 0.0004 | 530.0 | 530 | 5.4083 |
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+ | 0.0004 | 540.0 | 540 | 5.3947 |
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+ | 0.0004 | 550.0 | 550 | 5.4211 |
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+ | 0.0004 | 560.0 | 560 | 5.4287 |
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+ | 0.0004 | 570.0 | 570 | 5.4580 |
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+ | 0.0004 | 580.0 | 580 | 5.4610 |
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+ | 0.0004 | 590.0 | 590 | 5.4775 |
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+ | 0.0004 | 600.0 | 600 | 5.5165 |
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+ | 0.0004 | 610.0 | 610 | 5.5356 |
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+ | 0.0004 | 620.0 | 620 | 5.5142 |
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+ | 0.0004 | 630.0 | 630 | 5.4963 |
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+ | 0.0004 | 640.0 | 640 | 5.5114 |
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+ | 0.0004 | 650.0 | 650 | 5.5223 |
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+ | 0.0004 | 660.0 | 660 | 5.5468 |
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+ | 0.0004 | 670.0 | 670 | 5.5543 |
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+ | 0.0004 | 680.0 | 680 | 5.5731 |
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+ | 0.0004 | 690.0 | 690 | 5.6010 |
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+ | 0.0004 | 700.0 | 700 | 5.6050 |
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+ | 0.0004 | 710.0 | 710 | 5.6203 |
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+ | 0.0004 | 720.0 | 720 | 5.6415 |
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+ | 0.0004 | 730.0 | 730 | 5.6312 |
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+ | 0.0004 | 740.0 | 740 | 5.6209 |
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+ | 0.0004 | 750.0 | 750 | 5.6283 |
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+ | 0.0004 | 760.0 | 760 | 5.6605 |
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+ | 0.0004 | 770.0 | 770 | 5.6683 |
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+ | 0.0004 | 780.0 | 780 | 5.6686 |
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+ | 0.0004 | 790.0 | 790 | 5.6810 |
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+ | 0.0004 | 800.0 | 800 | 5.6837 |
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+ | 0.0004 | 810.0 | 810 | 5.7018 |
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+ | 0.0004 | 820.0 | 820 | 5.7189 |
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+ | 0.0004 | 830.0 | 830 | 5.7218 |
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+ | 0.0004 | 840.0 | 840 | 5.7053 |
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+ | 0.0004 | 850.0 | 850 | 5.7328 |
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+ | 0.0004 | 860.0 | 860 | 5.7495 |
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+ | 0.0004 | 870.0 | 870 | 5.7220 |
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+ | 0.0004 | 880.0 | 880 | 5.7142 |
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+ | 0.0004 | 890.0 | 890 | 5.7272 |
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+ | 0.0004 | 900.0 | 900 | 5.7643 |
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+ | 0.0004 | 910.0 | 910 | 5.7750 |
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+ | 0.0004 | 920.0 | 920 | 5.7762 |
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+ | 0.0004 | 930.0 | 930 | 5.7899 |
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+ | 0.0004 | 940.0 | 940 | 5.7878 |
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+ | 0.0004 | 950.0 | 950 | 5.7727 |
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+ | 0.0004 | 960.0 | 960 | 5.7630 |
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+ | 0.0004 | 970.0 | 970 | 5.7806 |
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+ | 0.0004 | 980.0 | 980 | 5.7953 |
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+ | 0.0004 | 990.0 | 990 | 5.7662 |
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+ | 0.0004 | 1000.0 | 1000 | 5.8028 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.9.0
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+ - Transformers 4.38.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
adapter_config.json ADDED
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+ {
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+ "alpha_pattern": {},
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+ "auto_mapping": null,
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+ "base_model_name_or_path": "google/gemma-2b-it",
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+ "bias": "none",
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+ "fan_in_fan_out": false,
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "layers_pattern": null,
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+ "layers_to_transform": null,
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+ "loftq_config": {},
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+ "lora_alpha": 20,
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+ "lora_dropout": 0.1,
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+ "megatron_config": null,
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+ "megatron_core": "megatron.core",
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+ "modules_to_save": [
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+ "lm_head",
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+ "embed_tokens"
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+ ],
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+ "peft_type": "LORA",
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+ "r": 16,
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+ "rank_pattern": {},
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+ "revision": null,
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+ "target_modules": [
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+ "down_proj",
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+ "q_proj",
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+ "up_proj",
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+ "v_proj",
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+ "k_proj",
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+ "o_proj",
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+ "gate_proj"
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+ ],
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+ "task_type": "CAUSAL_LM",
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+ "use_dora": false,
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+ "use_rslora": false
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+ }
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